Investigating multi-decadal climate variations in seasonal forecasts

在季节性时间尺度上可靠地预测天气和气候的能力具有重大的社会和科学意义。如果洪水、干旱和降雨可以提前一个季节或更长时间可靠地预测，这些事件的经济和社会成本就可以大大降低。例如，能够更好地预测南美洲北部、印度、南部非洲部分地区和澳大利亚的热带干旱，以及菲律宾和东南亚其他地区的热带气旋预警。由于气候变化，一些极端天气事件的频率和严重程度都在增加，这一点尤其值得关注。在过去的一个世纪里，季节性预测技术有了显著的提高，预报系统的连续几代都在提高预报的准确性和可能的时间长度。尤其在热带地区，可预测性的一个重要来源是厄尔Niño南方涛动（ENSO），它是热带东太平洋上空风和海温（SSTs）的不规则周期性变化。它是多年大气变率的最强模态，并通过其对大气和海洋环流模式的影响（称为远相关）影响全世界的气压、降水和温度。有证据表明，这些遥联系不是固定的，而是可以随着时间的推移而改变的。在世界其他地区，特别是北大西洋，ENSO、北大西洋涛动（NAO）和急流的强度和位置之间存在复杂的季节性依赖关系，因此预测能力普遍较低。诸如信噪矛盾（气候模式具有低信噪比，但仍然能够熟练地预测观测到的气候变率）和ENSO春季可预测性障碍（北方春季预测与观测之间的相关性显著降低）等问题已被确定，并成为开放研究的主题。本项目旨在调查和确定观测到的预报技能多年代际变化的大尺度大气和海洋动力原因，帮助了解影响这种气候状态和模式预报技能的低频波动的因素，并最终改善季节预报。

The ability to forecast weather and climate reliably on a seasonal timescale is of great societal and scientific interest. If floods, droughts, and rainfall onset can be reliably predicted a season or more in advance, the economic and social costs of these events can be significantly reduced. Examples include being able to better predict tropical droughts in northern South America, India, parts of southern Africa and Australie, and earlier warnings of tropical cyclones in the Philippines and other parts of southeast Asia. This is particularly topical as some extreme weather events increase in frequency and severity due to climate change.Seasonal predictive skill has improved markedly over the past century, with successive generations of forecast systems improving on the accuracy and length of forecasts that are possible. A significant source of predictability, especially in the tropics, is provided by El Niño Southern Oscillation (ENSO), an irregular periodic variation in winds and sea surface temperatures (SSTs) over the tropical eastern Pacific Ocean. It is the strongest mode of multi-annual atmospheric variability and affects air pressure, precipitation, and temperatures all over the world through its impact on atmospheric and oceanic circulation patterns, known as teleconnections. There is evidence that these teleconnections are not stationary but can change over time. There is generally lower predictive skill in other areas of the world, notably the North Atlantic, where there is a complex, seasonally dependent relationship between ENSO, the North Atlantic Oscillation (NAO) and the strength and position of the jet stream. Issues, such as the signal-to-noise paradox (climate models have a low signal-to-noise ratio, but nevertheless are able to skilfully predict observed climate variability) and the ENSO spring predictability barrier (the correlation between predictions and observations dramatically decreases in boreal spring), have been identified and are the subject of open research.This project aims to investigate and identify the large-scale atmospheric and oceanic dynamical reasons for the observed multi-decadal variations in forecast skill, helping to understand what factors influence such low-frequency fluctuations in the climate state and model forecast skill and, ultimately, how to improve seasonal forecasts.